首页|Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys

Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys

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Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium.However,it is still difficult to obtain novel near-β-Ti alloys with low modulus.This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD)to facilitate the design of near-β-Ti alloys.An elastic modulus database of Ti-Nb-Zr-Mo-Ta-Sn system was constructed first,and then three features(the electron to atom ratio,mean absolute devia-tion of atom mass,and mean electronegativity)were selected as the key factors of modulus by performing a three-step feature selection.With these features,a highly accurate model was built for predicting the modulus of near-β-Ti alloys.To further ensure the accuracy of mod-ulus prediction,machine learning with the elastic constants calculated was leveraged by CALPHAD database.The root mean square error of the well-trained model can be as low as 6.75 GPa.Guided by the prediction of machine learning and CALPHAD,three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study.The best candidate alloy(Ti-26Nb-4Zr-4Sn-1Mo-Ta)exhibits an ultra-low modulus(36.6 GPa)after cold rolling with a thickness reduction of 20％.Our method can greatly save time and resources in the development of novel Ti alloys,and experimental verifications have demonstrated the reliability of this method.

Near-β-Ti alloyMachine learningCalculation of phase diagramLow-modulus alloyFeature selection

Hao Zou、Yue-Yan Tian、Li-Gang Zhang、Ren-Hao Xue、Zi-Xuan Deng、Ming-Ming Lu、Jian-Xin Wang、Li-Bin Liu

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School of Computer Science and Engineering,Central South University,Changsha 410083,China

School of Materials Science and Engineering,Central South University,Changsha 410083,China

State Key Laboratory of Powder Metallurgy,Central South University,Changsha 410083,China

National Natural Science Foundation of ChinaNatural Science Foundation of Hunan Province,ChinaGuangxi Key Laboratory of Information Materials(Guilin University of Electronic Technology),China

520713392020JJ4739201009-K

2024

10.1007/s12598-023-02333-w

稀有金属(英文版)
中国有色金属学会

稀有金属(英文版)

CSTPCDEI
影响因子：0.801
ISSN：1001-0521
年,卷(期)：2024.43(1)
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